Computer-aided imaging diagnostic processing apparatus and computer-aided imaging diagnostic processing method

ABSTRACT

In consideration of the fact that a lung field varies in the density of sponge-like tissue depending on an individual or display region, an opacity curve which gives priority to a nodule candidate region or an extended nodule candidate region can be set by generating a histogram concerning a volume of interest which includes a foreground region, and using the statistical analysis result on the histogram as an objective index.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is based upon and claims the benefit of priority fromprior Japanese Patent Application No. 2006-148108, filed May 29, 2006,the entire contents of which are incorporated herein by reference.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention relates to a computer-aided imaging diagnosticprocessing apparatus and computer-aided imaging diagnostic processingmethod which can satisfactorily visualize a desired target by properlysetting parameters in volume rendering (VR) processing.

2. Description of the Related Art

Currently, in Japan, lung cancer has become the first cause of malignanttumor death and has increased steadily. This leads to strong socialdemands for not only prevention by anti-smoking measures but also earlydetection. In Japan, each municipality has practiced lung cancerscreening by plain chest radiograph and sputum cytology. A 1998 reportby “Study Group Concerning Cancer Screening Effectiveness Evaluation” ofthe Ministry of Health and Welfare concluded that current lung cancerscreening had little effect if any. X-ray computed tomography (CT) candetect a lung field type lung cancer more easily than a plain chestradiograph. However, this technique could not be used for screeningbecause it took a long imaging time before the advent of a helical scantype CT in 1990. Shortly after the advent of helical CT, a method ofimaging at a relatively low X-ray tube current (to be referred to aslow-dose helical CT hereinafter) has been developed to reduce exposureto radiation, and pilot studies have been made on lung cancer screeningusing this technique in Japan and the U.S. The study results havedemonstrated that low-dose helical CT has a higher lung cancer detectionrate than plain chest radiograph.

An increase in the number of CT detector rows after 1998 has shortenedthe time required for helical CT imaging. The latest multi-detectorhelical CT, such as 64 rows model, can image the entire lungs with analmost isotropic resolution of less than 1 mm in less than 10 sec.Technical innovation of CT raises the possibility that it can detectsmaller lung cancers. However, the multi-detector helical CT generatesroughly thousand images per scan, and hence the load required forinterpretation of radiograms greatly increases.

When a volume rendering image of a lung field is to be displayed byusing a conventional computer-aided imaging diagnostic processingapparatus, the apparatus uses a constant lung field opacity curve(opacity characteristic curve) regardless of density differences in thelung field or individual density differences in the lung field. In orderto obtain a desired volume rendering image, therefore, the user adjustsparameters (an opacity curve and the like) for each volume renderingoperation by using an imaging display apparatus user interface.

In volume rendering display of a CT lung field, it is necessary toobtain feature amounts of a volume of interest (VOI) to be displayed anddetermine parameters for volume rendering display on the basis of thesefeature amounts. It is, however, difficult to obtain a volume renderingimage desired by the user by using these feature amounts, and hence ithas required a very long time to adjust parameters by using an imagingdisplay apparatus user interface. This increases the load on a doctorwho interprets radiograms. For this reason, this technique is rarelyused for diagnosis. That is, volume rendering image generationcorresponding to each purpose is rarely used for diagnosis because itfurther increases the load on the doctor who interprets radiograms inspite of the fact that it can generate isotropic three-dimensional imagedata.

Under the circumstances, in order to establish low-dose helical CT as alung cancer screening method and allow the use of volume renderingimages for imaging diagnosis, demands have arisen for a method of easilydisplaying volume rendering images corresponding to purposes.

BRIEF SUMMARY OF THE INVENTION

The present invention has been made in consideration of the abovesituation, and has as its object to provide a computer-aided imagingdiagnostic processing apparatus and computer-aided imaging diagnosticprocessing method which can set image generation parameters which allowproper visualization of a target automatically or with minimumoperation.

According to an aspect of the present invention, it is provided that amedical image processing apparatus which generates an image on the basisof data acquired by using a medical imaging device comprises a regionspecifying unit which specifies a processing target region in an image,a parameter setting unit which executes statistical processingconcerning an image in the processing target region and sets an imagegeneration parameter on the basis of the statistical result, and animage generating unit which generates a projection image on the basis ofthe set image generation parameter.

According to another aspect of the present invention, it is providedthat a medical image processing method comprising specifying aprocessing target region in an image acquired by using a medical imagingdevice, executing statistical processing concerning an image in theprocessing target region and setting an image generation parameter onthe basis of the statistical result, and generating a projection imageon the basis of the set image generation parameter.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWING

FIG. 1 is a block diagram showing the arrangement of a computer-aidedimaging diagnostic processing apparatus 1 according to this embodiment;

FIG. 2A is a flowchart showing a processing procedure including thecomputer-aided imaging diagnostic processing;

FIG. 2B is a flowchart showing a processing procedure including manualoperation;

FIGS. 3A and 3B are flowcharts showing a lung field extractionprocessing procedure;

FIG. 4A is a view showing images represented by the three-dimensionalimage data acquired by a multislice CT 2, and FIG. 4B is a view showingforeground portion images segmented from the images in FIG. 4A;

FIG. 5 is a flowchart showing a VR parameter setting processingprocedure;

FIGS. 6A, 6B, and 6C are views each showing an example of a VOI which isan extracted volume of interest;

FIG. 7 is a view showing an example of a VOI extracted in step S31;

FIG. 8 is a view showing a foreground region of a VOI extracted in stepS31;

FIG. 9 is a graph showing an H.U. histogram concerning a VOI which is anextracted volume of interest;

FIG. 10 is a table showing general CT values (H.U. values);

FIG. 11 is a view showing an example of an input window for selecting anodule candidate region priority mode or an extended nodule candidateregion priority mode;

FIG. 12 is a graph for explaining statistical analysis executed in VRparameter setting processing in extended nodule candidate regionpriority mode;

FIG. 13 is a graph of a general probability density function;

FIG. 14 is a graph for explaining statistical analysis executed in VRparameter setting processing in nodule candidate region priority mode;

FIG. 15 is a graph for explaining an opacity curve set by VR parametersetting processing in the nodule candidate region priority mode;

FIG. 16 is a graph for explaining an opacity curve set by VR parametersetting processing in the extended nodule candidate region prioritymode;

FIG. 17 is a view showing an example of a VR image obtained inaccordance with VR parameters set in the nodule candidate regionpriority mode;

FIG. 18 is a view showing an example of a VR image obtained inaccordance with VR parameters set in the extended nodule candidateregion priority mode;

FIG. 19 is a view showing examples of an opacity curve other than alinear function;

FIG. 20 is a view showing an example of a window for selecting anopacity curve;

FIG. 21 is a graph showing an opacity curve with a gradient S1-1 and anH.U. value of −750 [H.U.] which makes opacity=0.0;

FIG. 22 is a graph showing an opacity curve with the gradient S1-1 andan H.U. value of −650 [H.U.] which makes opacity=0.0;

FIG. 23 is a graph showing an opacity curve with the gradient S1-1 andan H.U. value of −550 [H.U.] which makes opacity=0.0;

FIG. 24 is a graph showing an opacity curve with the gradient S1-1 andan H.U. value of −250 [H.U.] which makes opacity=0.0;

FIG. 25 is a graph showing an opacity curve with a gradient S2-1 and anH.U. value of −750 [H.U.] which makes opacity=0.0;

FIG. 26 is a graph showing an opacity curve with the gradient S2-1 andan H.U. value of −650 [H.U.] which makes opacity=0.0;

FIG. 27 is a graph showing an opacity curve with the gradient S1-1 andan H.U. value of −750 [H.U.] which makes opacity=0.0;

FIG. 28 is a graph showing an opacity curve with a gradient S3-1 and anH.U. value of −750 [H.U.] which makes opacity=0.0;

FIG. 29 is a graph showing an opacity curve with a gradient S6-1 and anH.U. value of −750 [H.U.] which makes opacity=0.0;

FIG. 30 is a graph showing an opacity curve obtained when the gradientis changed in six steps from S1 -1 to S6-1;

FIG. 31 is a graph for explaining control on the bin width of ahistogram;

FIG. 32 is a graph for explaining control on the bin width of ahistogram;

FIG. 33 is a flowchart showing a VR parameter setting processingprocedure according to the second embodiment;

FIG. 34 is a graph showing an example of a histogram with a bin width of20 in a case wherein the nodule candidate region priority mode isselected;

FIG. 35 is a graph showing an example of a histogram with a bin width of20 in a case wherein the extended nodule candidate region priority modeis selected;

FIG. 36 is a flowchart showing a VR parameter setting processingprocedure according to the third embodiment;

FIG. 37 is a view for explaining the effects of VR parameter settingprocessing according to the third embodiment; and

FIG. 38 is a view for explaining the effects of VR parameter settingprocessing according to the third embodiment.

DETAILED DESCRIPTION OF THE INVENTION

The first and second embodiments of the present invention will bedescribed below with reference to the views of the accompanying drawing.Note that in the following description, the same reference numeralsdenote constituent elements having almost the same functions andarrangements, and a repetitive description will be made only whenrequired.

For the sake of a concrete description, each embodiment will exemplify acase wherein a lung field is a diagnosis target. However, the presentinvention is not limited to this, and the technical idea of eachembodiment can be applied to a case wherein, for example, a breast orliver is a diagnosis target.

FIRST EMBODIMENT

FIG. 1 is a block diagram showing the arrangement of a computer-aidedimaging diagnostic processing apparatus 1 according to this embodiment.Referring to FIG. 1, the computer-aided imaging diagnostic processingapparatus 1 comprises a control unit 10, image processing unit 12,display unit 14, operation unit 16, storage unit 18,transmission/reception unit 19, lung field extraction unit 17,foreground region division unit 20, and data analyzing unit 21.

The computer-aided imaging diagnostic processing apparatus according tothis embodiment can use, for example, a general-purpose computerapparatus as basic hardware. The lung field extraction unit 17 and thedata analyzing unit 21 can be implemented by causing a processor mountedin the above computer apparatus to execute imaging diagnostic processingprograms. In this case, it suffices to implement the computer-aidedimaging diagnostic processing apparatus 1 by installing the aboveimaging diagnostic processing programs in the above computer apparatusin advance or by distributing the above imaging diagnostic processingprograms upon recording them on a removable recording medium such as amagnetic disk, magnetooptic disk, optical disk, or semiconductor memoryor through a network, and installing the programs in the above computerapparatus as needed. Note that some or all of the above units can beimplemented by hardware such as logic circuits. In addition, each of theabove units can be implemented by a combination of hardware and softwarecontrol.

The control unit 10 dynamically or statically controls the respectiveunits constituting the computer-aided imaging diagnostic processingapparatus. The control unit 10 comprehensively controls the lung fieldextraction unit 17, the data analyzing unit 21, and the like in lungfield extraction and VR parameter setting, in particular.

The image processing unit 12 performs predetermined image processingcorresponding to diagnosis purposes by using the images acquired byvarious kinds of medical imaging devices. The image processing unit 12executes VR processing in accordance with the opacity curve set by a VRparameter setting function, in particular. Performing image processingby using the image processing unit 12 will generate an image (diagnosticimage) used for imaging diagnosis.

The display unit 14 displays an input window forsetting/selecting/changing a histogram and opacity curve concerning apredetermined image and a predetermined region, an input window forperforming other operations, and the like in a predetermined form.

The operation unit 16 includes a trackball, various switches, a mouse, akeyboard, and the like for inputting various instructions, conditions,and the like from the operator to the apparatus 1. The operation unit 16also includes a predetermined interface for setting/selecting/changingan opacity curve and the like in VR parameter setting operation to bedescribed later.

The lung field extraction unit 17 implements the lung field extractionfunction under the control of the control unit 10. The processingimplemented by this lung field extraction function will be referred toas lung field extraction function.

The storage unit 18 stores patient data, image data acquired by variouskinds of medical imaging devices typified by a multislice CT 2, programsfor executing processing based on the lung field extraction function,various kinds of image processing typified by VR processing, and thelike, and a dedicated program for implementing a VR parameter settingfunction.

The transmission/reception unit 19 transmits/receives information whichcan be used for diagnosis to/from another apparatus or a databasethrough a network.

The foreground region division unit 20 extracts a lung field byexecuting predetermined image processing using the three-dimensionalimage data acquired by the X-ray CT apparatus, and segments theextracted lung field into a foreground region (a region almostcorresponding to lung blood vessels and a nodule) and a backgroundregion (a region other than the foreground region).

The data analyzing unit 21 implements the VR parameter setting function.This function extracts a region (nodule candidate region) which can be anodule and a region (extended nodule candidate region) comprising anodule candidate region and a surrounding region continuous with it byusing an image (region segmented image) segmented into a foregroundregion and a background region by lung field extraction processing, andperforms statistical analysis using a histogram concerning the H.U.(Hounsfield Unit) values of these regions, thereby determining a VRparameter (i.e., an opacity curve). The processing implemented by thisVR parameter setting function will be referred to as VR parametersetting processing.

The operation of the computer-aided imaging diagnostic processingapparatus 1 having the above arrangement will be described next.

FIG. 2 is a flowchart showing a processing procedure in a case whereinthe computer-aided imaging diagnostic processing apparatus 1 performsvolume rendering image generation/display. As shown in FIG. 2A and FIG.2B, in this volume rendering image generation/display, thecomputer-aided imaging diagnostic processing apparatus 1 performsprocesses, e.g., acquisition of three-dimensional image data by themultislice CT 2, lung field extraction, VR parameter setting, VRrendering image generation, and VR image display. The contents of eachprocess will be described below.

(Acquisition of X-ray CT Three-dimensional Image Data: Step S1)

The multislice CT 2 images the entire chest portion including the lungsof a subject as a diagnosis target, and performs predetermined imagereconstruction and the like, thereby acquiring three-dimensional imagedata. Note that the imaging method (e.g., a conventional scan method orhelical scan method) to be used and the image reconstruction method tobe used are not specifically limited. The storage unit 18 of thecomputer-aided imaging diagnostic processing apparatus 1 stores theacquired three-dimensional image data by network communication orthrough a portable external medium or the like.

(Lung Field Extraction Processing: Step S2)

FIG. 3A is a flowchart showing a lung field extraction processingprocedure. As shown in FIG. 3A, first of all, the lung field extractionunit 17 reads the three-dimensional image data acquired by, for example,the multislice CT from the storage unit 18 (step S21). The lung fieldextraction unit 17 then segments a region corresponding to the lungsfrom the above three-dimensional image data (step S22). This processingcan use an existing method (Hu S., Hoffman E. A., Reinhardt J. M.,“Automatic lung segmentation for accurate quantitation of volumetricX-ray CT images”, IEEE Trans Med Imaging 2001; 20: 490-498).

The lung field extraction unit 17 then segments the lung region obtainedin step S22 into a foreground region almost corresponding to lung bloodvessels and a nodule and a background region other than the foregroundregion (step S23). This processing can use, for example, existingadaptive threshold processing (Manay S., Yezzi A., “Antigeometricdiffusion for adaptive thresholding and fast segmentation”, IEEE TransImage Processing 2003; 12: 1310-1323). FIG. 4A is a view showing imagesusing the three-dimensional image data acquired by the multislice CT 2.FIG. 4B is a view showing images of the foreground region segmented fromthe image data shown in FIG. 4A. A nodule exists in each circle in FIG.4A. Referring to FIG. 4B, each black range corresponds to a lung region,and a hollow region in the lung region represents a lung regionforeground portion. Then the nodule which user wants to observe isselected.

Note that this lung field extraction processing can also be implementedby, for example, specifying a nodule candidate region by manualoperation through the operation unit 16 (step M1 shown in FIG. 2B).

FIG. 3B is a flowchart showing a lung field extraction processingprocedure including manual operation. As shown in FIG. 3B, first of all,the lung field extraction unit 17 reads three-dimensional image datafrom the storage unit 18. The display unit 14 displays athree-dimensional image based on the read data (step S21). When theoperator specifies a nodule candidate region on the displayedthree-dimensional image by manual operation using the operation unit 16,the control unit 10 receives the specifying instruction (step S22 a).The lung field extraction unit 17 extracts a nodule candidate regionfrom the three-dimensional image data on the basis of the specifiednodule candidate region (step S22 b), and segments the extracted nodulecandidate region into a foreground region and a background region (stepS23).

(VR Parameter Setting Function: Step S3)

FIG. 5 is a flowchart showing a VR parameter setting processingprocedure. As shown in FIG. 5, the data analyzing unit 21 determines thecenter and size of the VOI of a rectangular parallelepiped region in theregion segmented image generated in step S23 by using ellipseinformation obtained by the technique disclosed in Jpn. Pat. Appln.KOKAI Publication No. 2006-239005. FIG. 6A shows a cross-section of theVOI in FIG. 4A. FIG. 6B shows a cross-section of the VOI in FIG. 4B.FIG. 6C shows a foreground extraction cross-section using thecross-section in FIG. 4B. FIGS. 7 and 8 show VOIs extracted in step S31.

The data analyzing unit 21 then generates a histogram of H.U. valuesconcerning the extracted VOI which includes the only foreground region,as shown in, for example, FIG. 9 (step S32). Note that FIG. 10 showsgeneral CT values (i.e., H.U. values).

The data analyzing unit 21 then accepts the selection of a nodulecandidate region priority mode or an extended nodule candidate regionpriority mode (step S33). In this case, the nodule candidate regionpriority mode is a mode of setting an opacity curve for preferentiallyvisualizing a nodule candidate region. The extended nodule candidateregion priority mode is a mode of setting an opacity curve forpreferentially visualizing an extended nodule candidate region. In themode selection in this step, it is preferable to accept selectingoperation performed by a dedicated switch provided for the operationunit 16 or through an input window shown in FIG. 11. Selecting thenodule candidate region priority mode or the extended nodule candidateregion priority mode determines whether to preferentially visualize anodule candidate region or an extended nodule candidate region. For thisreason, selecting the nodule candidate region priority mode or theextended nodule candidate region priority mode amounts to selecting animage type.

The data analyzing unit 21 then executes statistical analysiscorresponding to the mode selected in step S33 by using an H.U.histogram concerning a generated VOI (step S34). Statistical analysisexecuted in this case is to acquire the average, standard deviation,variance, and the like of the histogram by performing fitting processingfor the histogram using a probability density function.

That is, when the nodule candidate region priority mode is selected, thedata analyzing unit 21 sets, as a value A, the H.U. value which ismaximized in a positive region on the histogram, and sets, as a regiona, a region ranging from a position corresponding to H.U.=0 to aposition corresponding to a total voxel count of 0 through a positioncorresponding to H.U.=B, as shown in FIG. 14 and FIG. 15. In addition,the data analyzing unit 21 performs statistical analysis of estimating aGaussian distribution function by performing fitting calculation usingthe probability density function (plotted in FIG. 15) based on equation(1) given below: $\begin{matrix}{{f(x)} = {A\quad{\mathbb{e}}^{\frac{- {({x - \mu_{a}})}^{2}}{\sigma_{a}^{2}}}}} & (1)\end{matrix}$where μ_(a) is an average, σ_(a) is a standard deviation, and σ_(a) ² isa variance.

When the extended nodule candidate region priority mode is selected, thedata analyzing unit 21 sets, as a value B and a value C, respectively,the H.U. value which is maximized in a negative region and the H.U.value which makes the total voxel count become 0 when H.U.=A or less,and sets L=|A−C|, as shown in FIG. 12 and FIG. 16. As shown in FIG. 14,the data analyzing unit 21 performs statistical analysis of estimating aGaussian distribution function by performing fitting calculation usingthe probability density function (see FIG. 16) based on equation (2)given below with respect to a region β ranging from C to 2 L:$\begin{matrix}{{f(x)} = {A\quad{\mathbb{e}}^{\frac{- {({x - \mu_{b}})}^{2}}{\sigma_{b}^{2}}}}} & (2)\end{matrix}$where μ_(b) is an average, σ_(b) is a standard deviation, and σ_(b) ² isa variance.

The data analyzing unit 21 then sets an opacity curve by determining anopacity window level (OWL) and an opacity window width (OWW) using thestatistical analysis result (step S34). That is, when the nodulecandidate region priority mode is selected, the data analyzing unit 21sets an opacity curve Co shown in FIG. 15 as OWL=μ_(a)−3σ_(a) andOWW=3σ_(a). When the extended nodule candidate region priority mode isselected, the data analyzing unit 21 sets an opacity curve Co shown inFIG. 16 as OWL=OWW=3σ_(b).

Note that the above determined values of OWL and OWW are examples, andother arbitrary real numbers can be selected as needed.

In addition, it suffices to obtain an opacity curve characteristic suchthat the value of opacity changes with a change in voxel value from theopacity value at the lowest voxel value in the window to the opacityvalue at the highest voxel value in the window. It suffices to linearlychange the value of opacity or change it in accordance with the curveobtained by using a predetermined curve function within this interval.

(VR Rendering Image Generation Processing: Step S4)

VR processing is executed by using the VR parameter calculated in stepS3, thereby generating a VR image. This processing can use the techniquedisclosed in John Pawasauskas, “Volume Visualization With Ray Casting”,CS563-Advanced Topics in Computer Graphics, Feb. 18, 1997.

(VR Image Display Processing: Step S5)

The display unit 14 displays the VR image generated in step S4 in apredetermined form. That is, when preferentially displaying a nodulecandidate region, the display unit 14 displays the VR image generated inaccordance with the opacity curve shown in FIG. 15 (see FIG. 17). Whenpreferentially displaying an extended nodule candidate region, thedisplay unit 14 displays the VR image generated in accordance with theopacity curve shown in FIG. 16 (see FIG. 18).

Note that the display unit 14 may simultaneously display the opacitycurve and VR image obtained by VR parameter setting (i.e., the opacitycurve shown in FIG. 15 [or FIG. 16] and the VR image or the VR image ofthe foreground region shown in FIG. 17 [or FIG. 18]).

(First Modification)

The above embodiment has exemplified the case wherein an opacity curvewhich is a linear function is set as a typical example. However, anopacity curve can be defined by a desired function or curve sketchinginstead of a linear function. FIG. 19 shows another typical example ofthe opacity curve. It is preferable from the viewpoint of operabilitythat a desired one of preset opacity curves can be selected by using auser interface like that shown in FIG. 20.

(Second Modification)

Performing predetermined operation makes it possible to change the VRparameter (opacity curve) set in accordance with the above embodiment toanother setting. The contents of this operation will be described belowwith reference to a case wherein an opacity curve is a linear function.

An opacity curve as a linear function can be changed in accordance withtwo condition settings. One setting is the gradient of an opacity curve,and the other setting is the H.U. value which makes opacity=0.

FIGS. 21 to 24 each show an opacity curve having a gradient S1-1 and theVR image generated by using the opacity curve, which are obtained whenthe H.U. value which makes opacity=0 is changed every 100 [H.U.] (note,however, that the H.U. value is changed in units of 300 [H.U.] in thecases shown in FIGS. 23 and 24). In the case shown in FIG. 21 (the lowerlimit of the opacity is −750 [H.U.] with the gradient S1-1), many bloodvessels around a nodule are displayed, and hence the nodule is hidden bya surrounding structure. In the case shown in FIG. 22 (the lower limitof the opacity is −650 [H.U.] with the gradient S1-1), the nodule iseasily viewable and the relationship between the nodule and the bloodvessels is easily comprehensible as compared with the case shown in FIG.21. In the case shown in FIG. 23 (the lower limit of the opacity is −550[H.U.] with the gradient S1-1), the nodule is more easily viewable. Inthe case shown in FIG. 24 (the lower limit of the opacity is −250 [H.U.]with the gradient S1-1), the nodule is visualized, but the blood vesselsare hidden.

FIG. 25 shows an opacity curve and the VR image generated by using theopacity curve in a case wherein the lower limit of the opacity is −750[H.U.] with a gradient S2-1. In this case, the blood vessels surroundingthe nodule are visualized so as to be easily viewable.

FIG. 26 shows an opacity curve and the VR image generated by using theopacity curve in a case wherein the lower limit of the opacity is −650[H.U.] with the gradient S2-1. In this case, the blood vesselssurrounding the nodule which are displayed reduce in number as comparedwith the case shown in FIG. 25, and the nodule and the relationshipbetween the nodule and the blood vessels are easily viewable.

FIGS. 27 to 29 each show an opacity curve and the VR image generated byusing the opacity curve in a case wherein the lower limit of the opacityis fixed to −750 [H.U.] and its gradient is changed in three steps(S1-1, S3-1, and S6-1). In the case in FIG. 27, many blood vessels andbronchi around the nodule are visualized, but the nodule itself ishidden. The image in FIG. 28 allows easy comprehension of therelationship between the nodule, its surrounding blood vessels, and thebronchi. Note that a region Q in FIG. 28 is a partial volume effectportion of peripheral bronchi or peripheral blood vessels. A VR imagegreatly changes depending on whether VR display includes this region. Inthe case in FIG. 29, the nodule is displayed in a form that is easy toobserve by not displaying any surrounding structure of the nodule.

For further reference, FIG. 30 shows how an opacity curve is displayedas the gradient is changed in six steps from S1-1 to S6-1.

(Effects)

According to the above arrangement, the following effects can beobtained.

In consideration of the fact that a lung field varies in the density ofsponge-like tissue depending on an individual or a display region, thiscomputer-aided imaging diagnostic processing apparatus can set anopacity curve which gives priority to a nodule candidate region or anextended nodule candidate region by generating a histogram concerning avolume of interest which includes a foreground region, and using thestatistical analysis result on the histogram as an objective index.Therefore, an opacity curve can be properly set in accordance with apurpose, e.g., an observation target. This makes it possible toimplement a computer-aided imaging diagnostic processing apparatus whichcan clearly visualize a nodule candidate region or an extended nodulecandidate region.

In addition, this computer-aided imaging diagnostic processing apparatusautomatically sets an opacity curve on the basis of a statisticalanalysis result on a histogram. This makes it possible to set VRparameters quickly and easily as compared with the prior art and toreduce the operation load on the user at the time of imaging diagnosis.

Furthermore, this computer-aided imaging diagnostic processing apparatuscan change an automatically set opacity curve on a histogram concerninga volume of interest. Therefore, the user can quickly and accuratelyperform fine adjustment and change of an opacity curve.

SECOND EMBODIMENT

The second embodiment of the present invention will be described next.This embodiment can set the bin width of a histogram to an arbitraryvalue in accordance with operation by the operator through an operationunit 16 in VR parameter setting. In this case, the bin width is thewidth of each class on the abscissa of the histogram. For example,changing a bin width of 1 like that shown in FIG. 31 to a bin width of20 will obtain a histogram like that shown in FIG. 32.

Note that a computer-aided imaging diagnostic processing apparatusaccording to this embodiment differs from that according to the firstembodiment only in VR parameter setting processing corresponding to stepS3 in FIG. 2.

FIG. 33 is a flowchart showing a VR parameter setting processingprocedure according to this embodiment. As shown in FIG. 33, first ofall, a data analyzing unit 21 extracts a region including a foregroundregion as a VOI from a region segmented image generated in step S23(step S31 a). This VOI extraction processing is the same as that in stepS31 in FIG. 5.

The data analyzing unit 21 then accepts the selection of the nodulecandidate region priority mode or the extended nodule candidate regionpriority mode (step S32 a).

The data analyzing unit 21 then generates a histogram with, for example,a bin width of 20 which is initially set, with respect to the extractedVOI (step S33 a). Note that the setting of a bin width of 20 is merelyan example, and the present invention is not limited to this. However,in VR parameter setting concerning a lung field, it is preferable to seta bin width to, for example, 10 or more and 20 or less.

The data analyzing unit 21 executes statistical analysis correspondingto the mode selected in step S33 a by using an H.U. histogram concerningthe generated VOI (step S34 a).

When the nodule candidate region priority mode is selected, the dataanalyzing unit 21 calculates a Gaussian distribution function, its peakvalue, and the like by using a histogram like that shown in FIG. 34 witha bin width of 20 and an abscissa range from −1000 [H.U.] to 200 [H.U.].When the extended nodule candidate region priority mode is selected, thedata analyzing unit 21 calculates a Gaussian distribution function, itspeak value, and the like by using a histogram like that shown in FIG. 35with a bin width of 20 and an abscissa range from −1000 [H.U.] to −500[H.U.].

The data analyzing unit 21 sets an opacity curve by determining an OWLand an OWW by using the statistical analysis result (step S35 a). Thisopacity curve setting processing is the same as that in step S34 in FIG.5.

According to the arrangement described above, this apparatus can set anopacity curve which gives priority to a nodule candidate region or anextended nodule candidate region by generating a histogram with adesired bin width which concerns a volume of interest and using thestatistical analysis result on the histogram as an objective index.Therefore, setting the bin width to a proper value makes it possible toprovide a highly reliable image with little influence from statisticalanalysis errors.

THIRD EMBODIMENT

The third embodiment of the present invention will be described next.This embodiment stores opacity curves (i.e., OWL values and OWW valuesfor the respective image types) for the respective image types obtainedby the technique according to the first or second embodiment incorrespondence with VR images, and allows the use of the opacity curvesfor the observation of the same region after the lapse of apredetermined period. An arrangement according to this embodiment iseffective for a case wherein, for example, imaging diagnosis is to beperformed on a patient's progress after operation or a temporal changein tumor is to be observed.

A computer-aided imaging diagnostic processing apparatus according tothis embodiment differs from that of the first embodiment only in VRparameter setting processing corresponding to step S3 in FIG. 2.

In addition, the form of storing OWL values and OWW values for therespective image types is not specifically limited. Typically, there isavailable a form of storing them as additional information of VR imagesin a storage unit 18 or storing them in correspondence with IDsspecifying VR images as files different from those of the VR images inthe storage unit 18.

FIG. 36 is a flowchart showing a VR parameter setting processingprocedure according to this embodiment. As shown in FIG. 36, first ofall, a control unit 10 acquires VR images used for past imagingdiagnosis and OWL values and OWW values for the respective image typesused for the generation of the VR images on the basis of the ID of thepatient, the examination ID, or the like (step S31 b).

A data analyzing unit 21 sets an opacity curve used for the currentimaging diagnosis by using the acquired OWL values and OWW values forthe respective image types (step S31 b).

FIG. 37 shows a VR image P1 concerning a nodule candidate region. FIG.38 shows a VR image P2 generated by using the image data obtained byimaging the same nodule candidate region six months after theacquisition of the VR image P1 shown in FIG. 37 and the opacity curveset by using the technique according to this embodiment. Comparing andobserving such images obtained with the same opacity curve at a giventime interval allows easy visual checking of a temporal change indiagnosis region.

FIG. 37 shows the VR image P1 concerning the nodule candidate region.FIG. 38 shows the VR image P2 generated by using the image data obtainedby imaging the same nodule candidate region six months after theacquisition of the VR image P1 shown in FIG. 37 and the opacity curveset by using the technique according to this embodiment. Comparing andobserving such images obtained with the same opacity curve at a giventime interval allow easy visual checking of a temporal change indiagnosis region when performing imaging diagnosis on the patient'sprogress after operation or observing a temporal change in tumor.

In addition, the arrangement according to this embodiment can quicklyand simply reproduce the same parameter settings as those for a VR imagebased on which past diagnosis was made. This makes it possible to reduceartificial load at the time of imaging diagnosis and provide highlyreliable diagnostic images.

Note that the present invention is not limited to the above embodiments,and constituent elements can be variously modified and embodied at theexecution stage within the spirit and scope of the invention. Thefollowing are concrete modifications.

(1) Each function described in each embodiment can also be implementedby installing programs for executing the respective processes in acomputer and unarchiving them in a memory. In this case, the programswhich can cause the computer to execute the corresponding techniques canbe distributed by being stored in recording media such as magnetic disks(floppy [registered trademark] disks, hard disks, and the like), opticaldisks (CD-ROMs, DVDs, and the like), and semiconductor memories.

(2) The above embodiments generate a histogram concerning the VOIincluded the only foreground region in a region segmented image.However, the present invention is not limited to this. For example, thepresent invention may generate a pulmonary mass and a blood vesselregion from an image and generate a histogram concerning the extractedregion. In addition, when an image includes the lobar fissure andpleura, the distribution of the histogram may become indistinct. Inorder to avoid such inconvenience, it suffices to generate a histogramafter the removal of the lobar fissure and pleura from an image byregion extraction processing.

In addition, various inventions can be formed by proper combinations ofa plurality of constituent elements disclosed in the above embodiments.For example, several constituent elements may be omitted from the allthe constituent elements in each embodiment. In addition, constituentelements of the different embodiments may be combined as needed.

1. A medical image processing apparatus which generates an image on thebasis of data acquired by using a medical imaging device, the apparatuscomprising: a region specifying unit which specifies a processing targetregion in an image; a parameter setting unit which executes statisticalprocessing concerning an image in the processing target region and setsan image generation parameter on the basis of the statistical result;and an image generating unit which generates a projection image on thebasis of the set image generation parameter.
 2. An apparatus accordingto claim 1, which further comprises a selection unit which selects adisplay image type, and in which the parameter setting unit executes thestatistical processing on the basis of the selected display image typeand image data in the processing target region.
 3. An apparatusaccording to claim 2, wherein the parameter setting unit extracts avolume of interest in the processing target region in which the displayimage type is specified, generates a histogram concerning the volume ofinterest, and calculates an image generation parameter in accordancewith the selected display image type on the basis of the histogram. 4.An apparatus according to claim 1, wherein the parameter setting unitsets the image generation parameter on the basis of a statisticcalculated by statistical analysis using the histogram.
 5. An apparatusaccording to claim 1, wherein the parameter setting unit executes, asthe statistical analysis, fitting based on a Gaussian function withreference to a predetermined position on the histogram and calculationof a statistic including an average value and a variance value, and setsa opacity curve in volume rendering processing on the basis of thecalculated statistic, and the image generating unit generates athree-dimensional image by executing volume rendering processing usingthe set opacity curve as the image generation processing.
 6. Anapparatus according to claim 2, wherein the selection unit selects oneof a first image type of preferentially visualizing an abnormalcandidate region and a second image type of preferentially visualizingthe abnormal candidate region and a surrounding region thereof.
 7. Anapparatus according to claim 3, which further comprises a designationunit which designates a shape of the opacity curve, and in which theparameter setting unit sets an opacity curve having the designatedshape.
 8. An apparatus according to claim 3, which further comprises achanging unit which changes the opacity curve set by the parametersetting unit, and in which the image generating unit generates thethree-dimensional image by executing volume rendering processing usingthe opacity curve changed by the changing unit.
 9. An apparatusaccording to claim 3, further comprising a display unit which displays agenerated three-dimensional image and the opacity curve used in theimage generation processing.
 10. An apparatus according to claim 3,further comprising a display unit which displays only an imagecorresponding to a volume of interest extracted by the extraction unit.11. An apparatus according to claim 3, further comprising a display unitwhich displays the histogram while superimposing characteristicinformation of opacity on the histogram.
 12. An apparatus according toclaim 11, wherein the parameter setting unit sets a width and positionof a window, and acquires an opacity characteristic curve such that avalue of opacity changes with a change in the voxel value in the window.13. An apparatus according to claim 3, wherein the region setting unitextracts a region almost corresponding to lung blood vessel and a noduleas the processing target region.
 14. An apparatus according to claim 3,wherein the parameter setting unit generates the histogram in accordancewith one of an arbitrary bin width set by a user through the settingunit and a predetermined bin width which is initially set.
 15. Anapparatus according to claim 3, wherein the parameter setting unitgenerates the histogram in accordance with a bin width of 10 inclusiveto 20 exclusive.
 16. An apparatus according to claim 1, furthercomprising a storage unit which stores the image generation parameterset by the parameter setting unit in correspondence with the image forwhich the statistical processing has been executed.
 17. An apparatusaccording to claim 1, wherein the image generating unit generates theprojection image by using an image different from the image for whichthe statistical processing has been executed and the image generationparameter concerning the image for which the statistical processing hasbeen executed.
 18. A medical image processing method comprising:specifying a processing target region in an image acquired by using amedical imaging device; executing statistical processing concerning animage in the processing target region and setting an image generationparameter on the basis of the statistical result; and generating aprojection image on the basis of the set image generation parameter.